# -*- encoding: utf-8 -*-
"""
@File    : keen_07_保存网络.py
@Time    : 2019/11/5 16:43
@Author  : Keen
@Software: PyCharm
"""

import torch
import matplotlib.pyplot as plt
# 用于设计随机初始化种子，在网络初始化参数时，用随机初始化种子来保证初始化每次均相同
torch.manual_seed(1)    # reproducible

# fake data
x = torch.unsqueeze(torch.linspace(-1, 1, 100), dim=1)  # x data (tensor), shape=(100, 1)
y = x.pow(2) + 0.2*torch.rand(x.size())  # noisy y data (tensor), shape=(100, 1)

# print(x)
# print(y)


def save():
    # save net1
    net1 = torch.nn.Sequential(
        torch.nn.Linear(1, 10),
        torch.nn.ReLU(),
        torch.nn.Linear(10, 1)
    )
    optimizer = torch.optim.SGD(net1.parameters(), lr=0.5)  # learning rate
    loss_func = torch.nn.MSELoss()

    for t in range(100):
        prediction = net1(x)  # 预测的输出值
        loss = loss_func(prediction, y)  # 输出值与真实值求损失
        optimizer.zero_grad()  # 清空梯度
        loss.backward()  # 反向传播
        optimizer.step()  # 优化器进行下一步
    # plot result
    plt.figure(1, figsize=(10, 3))
    plt.subplot(131)
    plt.title('Net1')
    plt.scatter(x, y)
    # plt.plot(x, prediction, 'r-', lw=5)  # 红色实线

    # 2 ways to save the net
    torch.save(net1, 'net.pkl')  # save entire net
    torch.save(net1.state_dict(), 'net_params.pkl')   # save only the parameters


def restore_net():
    # restore entire net1 to net2
    net2 = torch.load('net.pkl')
    prediction = net2(x)

    # plot result
    plt.subplot(132)
    plt.title('Net2')
    plt.scatter(x.data.numpy(), y.data.numpy())
    plt.plot(x.data.numpy(), prediction.data.numpy(), 'r-', lw=5)


def restore_params():
    # restore only the parameters in net1 to net3
    net3 = torch.nn.Sequential(
        torch.nn.Linear(1, 10),
        torch.nn.ReLU(),
        torch.nn.Linear(10, 1)
    )

    # copy net1's parameters into net3
    net3.load_state_dict(torch.load('net_params.pkl'))
    prediction = net3(x)

    # plot result
    plt.subplot(133)
    plt.title('Net3')
    plt.scatter(x.data.numpy(), y.data.numpy())
    plt.plot(x.data.numpy(), prediction.data.numpy(), 'r-', lw=5)
    plt.show()


# save net1
save()

# restore entire net (may slow)
restore_net()

# restore only the net parameters
restore_params()
